Dataset Information

Screening and identification of critical biomarkers in erectile dysfunction: evidence from bioinformatic analysis.

ABSTRACT: Purpose:Erectile dysfunction (ED) is one of the most common male-disease globally. Despite efforts to explain its pathogenesis, the molecular mechanisms of ED are still not well understood. Methods:The microarray dataset GSE10804 was downloaded from the Gene Expression Omnibus (GEO) to find candidate genes in ED progression. After differentially expressed genes (DEGs) were identified, functional enrichment analysis was performed. In addition, a protein-protein interaction network (PPI) was established and module analysis was performed through the STRING and Cytoscape. Results and Conclusions:A total of 618 DEGs were identified in all, containing 430 downregulated genes and 188 upregulated genes. The enriched functions and pathways of the DEGs include transcription from RNA polymerase II promoter, cell adhesion, calcium ion binding, receptor binding, Akt signaling pathway, receptor interaction, protein digestion, and absorption. We picked out twenty-five hub genes, with biological process (BP) analyses revealing that the genes were principally associated with cellular responses to amino acid stimuli, extracellular matrix structural constituent, collagen trimer, protein digestion and absorption, ECM-receptor interaction and PI3K-Akt signaling pathway. To sum up, DEGs and hub genes distinguished in this study not only help us understand the molecular mechanisms behind the carcinogenesis and progression of ED, but also play a part in the diagnosis and treatment of ED by providing candidate targets.

SUBMITTER: Hui J

PROVIDER: S-EPMC7050549 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Screening and identification of critical biomarkers in erectile dysfunction: evidence from bioinformatic analysis.

Hui Jialiang J Liu Ruiyu R Zhang Haibo H He Shuhua S Wei Anyang A

PeerJ 20200228

<h4>Purpose</h4>Erectile dysfunction (ED) is one of the most common male-disease globally. Despite efforts to explain its pathogenesis, the molecular mechanisms of ED are still not well understood.<h4>Methods</h4>The microarray dataset GSE10804 was downloaded from the Gene Expression Omnibus (GEO) to find candidate genes in ED progression. After differentially expressed genes (DEGs) were identified, functional enrichment analysis was performed. In addition, a protein-protein interaction network ...[more]

PMID: 32161689

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Project description:BackgroundPapillary renal cell carcinoma (PRCC) is the most common type of renal cell carcinoma after clear cell renal cell carcinoma (ccRCC). Its pathological classification is controversial, and its molecular mechanism is poorly understood. Therefore, the identification of key genes and their biological pathways is of great significance to elucidate the molecular mechanisms of PRCC occurrence and progression.MethodsThe PRCC-related datasets GSE7023, GSE48352 and GSE15641 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape and STRING were used to construct the protein-protein interaction network (PPI) and perform module analysis to identify hub genes and key pathways. A heatmap of hub genes was constructed using the UCSC cancer genomics browser. Overall survival and recurrence-free survival of patients stratified by the expression levels of hub genes were analysed using Kaplan-Meier Plotter. The online database UALCAN was applied to analyse gene expression based on tissue type, stage, subtype and race.ResultsA total of 214 DEGs, specifically, 205 downregulated genes and 9 upregulated genes, were identified. The DEGs were mainly enriched in angiogenesis, kidney development, oxidation-reduction process, metabolic pathways, etc. The 17 hub genes identified were mainly enriched in the biological processes of angiogenesis, cell adhesion, platelet degranulation, and leukocyte transendothelial migration. Survival analysis showed that EGF, KDR, CXCL12, REN, PECAM1, CDH5, THY1, WT1, PLAU and DCN might be related to the carcinogenesis, metastasis or recurrence of PRCC. UALCAN analysis showed that low expression of PECAM1 and PLAU in PRCC tissues was related to stage, subtype and race.ConclusionsThe DEGs and hub genes identified in the present study provide insight into the specific molecular mechanisms of PRCC occurrence and development and may be potential molecular markers and therapeutic targets for the accurate classification and efficient diagnosis and treatment of PRCC.

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Screening and identification of critical biomarkers in erectile dysfunction: evidence from bioinformatic analysis.

Publications

Screening and identification of critical biomarkers in erectile dysfunction: evidence from bioinformatic analysis.

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